In this research a hybrid RKH-LSVR model is introduced. The RKH algorithm is a novel metaheuristic optimization technique inspired by the behaviour of krill herds. The RKH is used to optimize the LSVR parameters by balancing the search between local and global optima. The proposed model is evaluated through three different fitness functions, while its statistical and trading performance is benchmarked against a set of traditional SVR structures, non-linear and linear models and a RW. The inputs of the SVR models are selected through a large pool of linear and non-linear predictors and PCA analysis. All models are applied in four forecasting and trading exercises over six ETFs during the period 2010-2015. The purpose of the trading applications is to test the robustness of the models under study and to provide empirical evidence in favour of the AMH.
In terms of the results, RKH-LSVR architectures outperform their counterparts in terms of statistical accuracy and trading efficiency. The trading application provides evidences in favour of the AMH. This work should go forward on convincing researchers, practitioners and academics to explore further hybrid SVR techniques. It should also serve as caution on the implications of the AMH and the robustness of trading models.
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